## PRD: Configurable Hysteresis Parameters for CPU Frequency Scaling

This PRD focuses on making the hysteresis parameters implemented in GPM-048 configurable, allowing for finer control over CPU frequency scaling behavior.

### GPM-049: Introduce Configurable Hysteresis Up Threshold
**Goal:** Allow configuration of the hysteresis threshold required to *increase* CPU frequency.
**Acceptance Criteria:**
- A new configuration parameter, `hysteresis_up_threshold`, is added to the system configuration.  Default value is 5%.
- The frequency scaling logic uses `hysteresis_up_threshold` when determining if the CPU frequency should be increased.
- The system can successfully read and apply the `hysteresis_up_threshold` from a configuration file.
**Files:** `config/default_config.ini`, `src/frequency_scaling.py`
**Spawns:**  Implement a mechanism to dynamically update the hysteresis parameters without restarting the system.

### GPM-050: Introduce Configurable Hysteresis Down Threshold
**Goal:** Allow configuration of the hysteresis threshold required to *decrease* CPU frequency.
**Acceptance Criteria:**
- A new configuration parameter, `hysteresis_down_threshold`, is added to the system configuration. Default value is 1%.
- The frequency scaling logic uses `hysteresis_down_threshold` when determining if the CPU frequency should be decreased.
- The system can successfully read and apply the `hysteresis_down_threshold` from a configuration file.
**Files:** `config/default_config.ini`, `src/frequency_scaling.py`
**Spawns:** Introduce configurable hysteresis duration.

### GPM-051: Validate Hysteresis Threshold Values
**Goal:** Ensure that the configured hysteresis threshold values are within acceptable bounds.
**Acceptance Criteria:**
- The system validates that `hysteresis_up_threshold` and `hysteresis_down_threshold` are within a reasonable range (e.g., 0-100%).
- If an invalid threshold value is configured, the system logs an error message and reverts to the default value.
- Unit tests are added to verify the validation logic.
**Files:** `src/config_manager.py`, `tests/test_config_manager.py`
**Spawns:** Implement different scaling governors (e.g., ondemand, performance, powersave).

### GPM-052: Implement Unit Tests for Hysteresis Logic
**Goal:** Create unit tests to verify the correct behavior of the hysteresis logic in the frequency scaling module.
**Acceptance Criteria:**
- Unit tests cover cases where frequency should increase, decrease, and remain the same, considering different threshold values and CPU utilization levels.
- Code coverage for `src/frequency_scaling.py` increases by at least 20%.
- All new and existing unit tests pass.
**Files:** `tests/test_frequency_scaling.py`
**Spawns:** Implement a mechanism to automatically tune hysteresis parameters based on system workload.

### GPM-053: Add Logging for Hysteresis Events
**Goal:** Add informative logging to track when the hysteresis thresholds are triggered, and the resulting frequency scaling actions.
**Acceptance Criteria:**
- Log messages are generated when the CPU frequency is increased or decreased due to hysteresis.
- The log messages include relevant information such as the current CPU utilization, the hysteresis threshold values, and the previous and current CPU frequencies.
- Logging level is configurable.
**Files:** `src/frequency_scaling.py`
**Spawns:** Implement a dashboard to visualize the CPU frequency scaling behavior and hysteresis events.

### GPM-054: Add Documentation for Hysteresis Configuration
**Goal:** Document the new configuration parameters and their impact on CPU frequency scaling behavior.
**Acceptance Criteria:**
- The `hysteresis_up_threshold` and `hysteresis_down_threshold` parameters are documented in the system documentation.
- The documentation includes examples of how to configure these parameters and the expected results.
- The documentation explains the purpose of hysteresis in CPU frequency scaling.
**Files:** `docs/configuration.md`
**Spawns:** Develop a performance analysis tool to measure the impact of different hysteresis configurations on power consumption and performance.
